Le dioxyde de carbone (CO₂) est l’un des gaz à effet de serre les plus importants. Sa concentration atmosphérique a augmenté de manière significative depuis l’ère industrielle. Ce rapport analyse les données globales de concentrations de CO₂ collectées par le NOAA Global Monitoring Laboratory de 1979 à 2025.
# Liste des packages nécessaires
packages <- c("tidyverse", "lubridate", "ggplot2", "ggpubr", "zoo", "gridExtra",
"viridis", "scales", "forecast", "grid", "knitr", "kableExtra")
# Installer les packages manquants
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) {
install.packages(new_packages, repos = "https://cloud.r-project.org/")
}
# Charger tous les packages
invisible(lapply(packages, library, character.only = TRUE))
cat("✓ Tous les packages sont chargés\n")## ✓ Tous les packages sont chargés
# Vérifier l'existence du fichier
if(!file.exists("co2_mm_gl.csv")) {
stop("ERREUR: Fichier co2_mm_gl.csv non trouvé!")
}
# Inspection des premières lignes
cat("=== INSPECTION DES DONNÉES BRUTES ===\n\n")## === INSPECTION DES DONNÉES BRUTES ===
## Premières lignes du fichier:
## Ligne 1: # --------------------------------------------------------------------
## Ligne 2: # USE OF NOAA GML DATA
## Ligne 3: #
## Ligne 4: # These data are made freely available to the public and the scientific
## Ligne 5: # community in the belief that their wide dissemination will lead to
## Ligne 6: # greater understanding and new scientific insights. To ensure that GML
## Ligne 7: # receives fair credit for their work please include relevant citation
## Ligne 8: # text in publications. We encourage users to contact the data providers,
## Ligne 9: # who can provide detailed information about the measurements and
## Ligne 10: # scientific insight. In cases where the data are central to a
# Lire les données (ignorer les lignes de commentaire)
co2_raw <- read.table("co2_mm_gl.csv",
header = TRUE,
sep = ",",
comment.char = "#",
stringsAsFactors = FALSE,
na.strings = c("", "NA", "-99.99"))
cat("\n=== DONNÉES BRUTES CHARGÉES ===\n")##
## === DONNÉES BRUTES CHARGÉES ===
## Dimensions: 561 lignes x 7 colonnes
## Période: 1979 à 2025
## Colonnes: year, month, decimal, average, average_unc, trend, trend_unc
co2_clean <- co2_raw %>%
# Créer une date complète
mutate(
Date = make_date(year, month, 15), # 15 du mois comme convention
Date_decimal = decimal,
# Variables principales
CO2_ppm = average, # Concentration CO₂
CO2_uncertainty = average_unc, # Incertitude
CO2_trend = trend, # Tendance désaisonnalisée
Trend_uncertainty = trend_unc,
# Identifiants et métadonnées
ID = row_number(),
# Variables temporelles
Year = year,
Month = month,
Quarter = ceiling(month / 3),
# Saisons (hémisphère nord)
Season = case_when(
month %in% c(12, 1, 2) ~ "Hiver",
month %in% c(3, 4, 5) ~ "Printemps",
month %in% c(6, 7, 8) ~ "Été",
month %in% c(9, 10, 11) ~ "Automne"
),
# Décennies
Decade = floor(Year / 10) * 10,
# Périodes d'analyse
Period = case_when(
Year < 1990 ~ "1979-1989",
Year >= 1990 & Year < 2000 ~ "1990-1999",
Year >= 2000 & Year < 2010 ~ "2000-2009",
Year >= 2010 & Year < 2020 ~ "2010-2019",
Year >= 2020 ~ "2020-2025"
),
# Variables dérivées
CO2_anomaly = CO2_ppm - mean(CO2_ppm, na.rm = TRUE),
Year_fraction = Year + (Month - 0.5) / 12
) %>%
# Gérer les valeurs manquantes
group_by(Year) %>%
mutate(
CO2_ppm = ifelse(is.na(CO2_ppm), mean(CO2_ppm, na.rm = TRUE), CO2_ppm)
) %>%
ungroup() %>%
# Sélectionner et ordonner les colonnes
select(ID, Date, Year, Month, Quarter, Season, Decade, Period,
CO2_ppm, CO2_uncertainty, CO2_trend, Trend_uncertainty,
CO2_anomaly, Date_decimal, Year_fraction) %>%
# Trier par date
arrange(Date)
cat("✓ Données nettoyées\n")## ✓ Données nettoyées
## Observations: 561
## Période finale: 1979 - 2025
## Valeurs manquantes CO2: 0
general_stats <- summary(co2_clean$CO2_ppm)
stats_table <- data.frame(
"Statistique" = c("Minimum", "Quartile 1", "Médiane", "Moyenne", "Quartile 3", "Maximum"),
"Valeur (ppm)" = c(
round(general_stats[1], 2),
round(general_stats[2], 2),
round(general_stats[3], 2),
round(general_stats[4], 2),
round(general_stats[5], 2),
round(general_stats[6], 2)
)
)
knitr::kable(stats_table, caption = "Statistiques descriptives du CO₂ global") %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed"))| Statistique | Valeur..ppm. | |
|---|---|---|
| Min. | Minimum | 334.37 |
| 1st Qu. | Quartile 1 | 354.33 |
| Median | Médiane | 372.51 |
| Mean | Moyenne | 375.67 |
| 3rd Qu. | Quartile 3 | 396.38 |
| Max. | Maximum | 426.87 |
decade_stats <- co2_clean %>%
group_by(Decade) %>%
summarise(
"Décennie" = paste0(Decade, "s"),
"Début" = min(Year),
"Fin" = max(Year),
"N mois" = n(),
"CO₂ moyen (ppm)" = round(mean(CO2_ppm, na.rm = TRUE), 2),
"Min (ppm)" = round(min(CO2_ppm, na.rm = TRUE), 2),
"Max (ppm)" = round(max(CO2_ppm, na.rm = TRUE), 2),
"Écart-type" = round(sd(CO2_ppm, na.rm = TRUE), 2),
"Augmentation (ppm)" = round(max(CO2_ppm, na.rm = TRUE) - min(CO2_ppm, na.rm = TRUE), 2),
.groups = 'drop'
) %>%
select(-Decade)
knitr::kable(decade_stats, caption = "Statistiques du CO₂ par décennie") %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))| Décennie | Début | Fin | N mois | CO₂ moyen (ppm) | Min (ppm) | Max (ppm) | Écart-type | Augmentation (ppm) |
|---|---|---|---|---|---|---|---|---|
| 1970s | 1979 | 1979 | 12 | 336.86 | 334.37 | 338.32 | 1.30 | 3.95 |
| 1970s | 1979 | 1979 | 12 | 336.86 | 334.37 | 338.32 | 1.30 | 3.95 |
| 1970s | 1979 | 1979 | 12 | 336.86 | 334.37 | 338.32 | 1.30 | 3.95 |
| 1970s | 1979 | 1979 | 12 | 336.86 | 334.37 | 338.32 | 1.30 | 3.95 |
| 1970s | 1979 | 1979 | 12 | 336.86 | 334.37 | 338.32 | 1.30 | 3.95 |
| 1970s | 1979 | 1979 | 12 | 336.86 | 334.37 | 338.32 | 1.30 | 3.95 |
| 1970s | 1979 | 1979 | 12 | 336.86 | 334.37 | 338.32 | 1.30 | 3.95 |
| 1970s | 1979 | 1979 | 12 | 336.86 | 334.37 | 338.32 | 1.30 | 3.95 |
| 1970s | 1979 | 1979 | 12 | 336.86 | 334.37 | 338.32 | 1.30 | 3.95 |
| 1970s | 1979 | 1979 | 12 | 336.86 | 334.37 | 338.32 | 1.30 | 3.95 |
| 1970s | 1979 | 1979 | 12 | 336.86 | 334.37 | 338.32 | 1.30 | 3.95 |
| 1970s | 1979 | 1979 | 12 | 336.86 | 334.37 | 338.32 | 1.30 | 3.95 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1980s | 1980 | 1989 | 120 | 345.16 | 337.05 | 354.38 | 4.66 | 17.33 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 1990s | 1990 | 1999 | 120 | 359.93 | 351.58 | 369.29 | 4.58 | 17.71 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2000s | 2000 | 2009 | 120 | 377.87 | 366.71 | 387.99 | 5.94 | 21.28 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2010s | 2010 | 2019 | 120 | 399.04 | 386.23 | 411.76 | 7.11 | 25.53 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
| 2020s | 2020 | 2025 | 69 | 418.34 | 409.73 | 426.87 | 4.60 | 17.14 |
season_stats <- co2_clean %>%
group_by(Season) %>%
summarise(
"CO₂ moyen (ppm)" = round(mean(CO2_ppm, na.rm = TRUE), 2),
"Écart-type" = round(sd(CO2_ppm, na.rm = TRUE), 2),
"Min (ppm)" = round(min(CO2_ppm, na.rm = TRUE), 2),
"Max (ppm)" = round(max(CO2_ppm, na.rm = TRUE), 2),
"Amplitude (ppm)" = round(max(CO2_ppm, na.rm = TRUE) - min(CO2_ppm, na.rm = TRUE), 2),
.groups = 'drop'
) %>%
arrange(`CO₂ moyen (ppm)`)
knitr::kable(season_stats, caption = "Statistiques saisonnières du CO₂") %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))| Season | CO₂ moyen (ppm) | Écart-type | Min (ppm) | Max (ppm) | Amplitude (ppm) |
|---|---|---|---|---|---|
| Automne | 374.32 | 25.42 | 335.02 | 424.07 | 89.05 |
| Été | 374.96 | 25.76 | 334.37 | 425.87 | 91.50 |
| Hiver | 376.21 | 25.70 | 336.56 | 426.42 | 89.86 |
| Printemps | 377.16 | 25.83 | 337.88 | 426.87 | 88.99 |
annual_stats <- co2_clean %>%
group_by(Year) %>%
summarise(
"CO₂ moyen (ppm)" = round(mean(CO2_ppm, na.rm = TRUE), 2),
"Min (ppm)" = round(min(CO2_ppm, na.rm = TRUE), 2),
"Max (ppm)" = round(max(CO2_ppm, na.rm = TRUE), 2),
"Amplitude (ppm)" = round(max(CO2_ppm, na.rm = TRUE) - min(CO2_ppm, na.rm = TRUE), 2),
.groups = 'drop'
) %>%
arrange(desc(`CO₂ moyen (ppm)`))
knitr::kable(head(annual_stats, 5), caption = "Top 5 des années avec concentrations les plus élevées") %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))| Year | CO₂ moyen (ppm) | Min (ppm) | Max (ppm) | Amplitude (ppm) |
|---|---|---|---|---|
| 2025 | 425.43 | 423.03 | 426.87 | 3.84 |
| 2024 | 422.80 | 420.27 | 425.19 | 4.92 |
| 2023 | 419.37 | 416.79 | 421.50 | 4.71 |
| 2022 | 417.09 | 414.40 | 418.93 | 4.53 |
| 2021 | 414.70 | 412.15 | 416.60 | 4.45 |
# Régression linéaire
lm_model <- lm(CO2_ppm ~ Year_fraction, data = co2_clean)
lm_summary <- summary(lm_model)
trend_results <- data.frame(
"Paramètre" = c("Pente", "Ordonnée à l'origine", "R²", "p-value"),
"Valeur" = c(
round(coef(lm_model)[2], 4),
round(coef(lm_model)[1], 2),
round(lm_summary$r.squared, 4),
format.pval(lm_summary$coefficients[2, 4], digits = 3)
),
"Interprétation" = c(
"ppm/année",
"ppm (1900)",
"ajustement du modèle",
"très significatif"
)
)
knitr::kable(trend_results, caption = "Résultats de la régression linéaire CO₂ ~ Temps") %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))| Paramètre | Valeur | Interprétation |
|---|---|---|
| Pente | 1.8838 | ppm/année |
| Ordonnée à l’origine | -3396.42 | ppm (1900) |
| R² | 0.9853 | ajustement du modèle |
| p-value | <0.0000000000000002 | très significatif |
Interprétation: La concentration de CO₂ augmente de 1.884 ppm par année avec une très bonne corrélation (R² = 0.985).
co2_1979 <- mean(co2_clean$CO2_ppm[co2_clean$Year == 1979], na.rm = TRUE)
co2_2025 <- mean(co2_clean$CO2_ppm[co2_clean$Year == 2025], na.rm = TRUE)
augmentation_abs <- co2_2025 - co2_1979
augmentation_pct <- (co2_2025 - co2_1979) / co2_1979 * 100
taux_annuel <- (co2_2025 - co2_1979) / (2025 - 1979)
augmentation_table <- data.frame(
"Année" = c("1979", "2025", "Différence"),
"CO₂ (ppm)" = c(
round(co2_1979, 1),
round(co2_2025, 1),
round(augmentation_abs, 1)
),
"% d'augmentation" = c(
"-",
"-",
round(augmentation_pct, 1)
)
)
knitr::kable(augmentation_table, caption = "Augmentation du CO₂ de 1979 à 2025") %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))| Année | CO…ppm. | X..d.augmentation |
|---|---|---|
| 1979 | 336.9 |
|
| 2025 | 425.4 |
|
| Différence | 88.6 | 26.3 |
##
## **Taux annuel moyen:** 1.93 ppm/an
seasonal_amplitude <- max(season_stats$`CO₂ moyen (ppm)`) - min(season_stats$`CO₂ moyen (ppm)`)
saisonnalite_table <- data.frame(
"Saison la plus élevée" = season_stats$Season[which.max(season_stats$`CO₂ moyen (ppm)`)],
"CO₂ max (ppm)" = round(max(season_stats$`CO₂ moyen (ppm)`), 2),
"Saison la plus basse" = season_stats$Season[which.min(season_stats$`CO₂ moyen (ppm)`)],
"CO₂ min (ppm)" = round(min(season_stats$`CO₂ moyen (ppm)`), 2),
"Amplitude (ppm)" = round(seasonal_amplitude, 2)
)
knitr::kable(saisonnalite_table, caption = "Analyse de la saisonnalité du CO₂") %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))| Saison.la.plus.élevée | CO..max..ppm. | Saison.la.plus.basse | CO..min..ppm. | Amplitude..ppm. |
|---|---|---|---|---|
| Printemps | 377.16 | Automne | 374.32 | 2.84 |
p1 <- ggplot(co2_clean, aes(x = Date, y = CO2_ppm)) +
geom_line(color = "#E41A1C", linewidth = 1, alpha = 0.8) +
geom_smooth(method = "loess", span = 0.1, color = "#377EB8",
linetype = "dashed", se = FALSE, linewidth = 1) +
geom_smooth(method = "lm", color = "#4DAF4A", se = FALSE, linewidth = 0.8) +
labs(
title = "Évolution des concentrations CO₂ globales (1979-2025)",
subtitle = "Source: NOAA Global Monitoring Laboratory",
x = "Année",
y = "CO₂ (ppm)",
caption = paste("Pente:", round(coef(lm_model)[2], 3), "ppm/an | R²:", round(lm_summary$r.squared, 3))
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(face = "bold", hjust = 0.5, size = 16),
plot.subtitle = element_text(hjust = 0.5, color = "gray40"),
plot.caption = element_text(hjust = 1, color = "gray50"),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "white", color = NA)
) +
scale_y_continuous(limits = c(330, 440)) +
scale_x_date(date_breaks = "5 years", date_labels = "%Y")
print(p1)Commentaire: Ce graphique démontre l’augmentation anthropogénique incontestable du CO₂ à l’échelle globale, avec une tendance robuste et systématique sur 46 ans.
p2 <- ggplot(co2_clean, aes(x = Month, y = CO2_ppm)) +
geom_jitter(alpha = 0.1, color = "gray70", width = 0.2) +
stat_summary(fun = mean, geom = "line", aes(group = 1),
color = "#984EA3", linewidth = 1.5) +
stat_summary(fun = mean, geom = "point", aes(group = 1),
color = "#984EA3", size = 3) +
stat_summary(fun.data = mean_cl_normal, geom = "ribbon",
aes(group = 1), alpha = 0.2, fill = "#984EA3") +
labs(
title = "Cycle saisonnier du CO₂ global",
subtitle = "Moyenne mensuelle avec intervalle de confiance",
x = "Mois",
y = "CO₂ (ppm)"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1)
) +
scale_x_continuous(breaks = 1:12, labels = month.abb)
print(p2)Commentaire:Ce graphique révèle le cycle saisonnier du CO₂ en montrant la moyenne mensuelle sur l’ensemble de la période 1979-2025. Le cycle saisonnier montre un maximum au printemps et un minimum en automne, reflétant les cycles de photosynthèse de l’hémisphère nord. Ce graphique démontre que malgré l’augmentation globale du CO₂, les cycles biologiques naturels continuent de fonctionner régulièrement, créant une oscillation saisonnière mesurable et fiable.
seasonal_by_decade <- co2_clean %>%
group_by(Decade, Month) %>%
summarise(
CO2_mean = mean(CO2_ppm, na.rm = TRUE),
CO2_sd = sd(CO2_ppm, na.rm = TRUE),
N = n(),
.groups = 'drop'
) %>%
mutate(
Month_name = factor(month.abb[Month], levels = month.abb),
Decade_label = paste0(Decade, "s")
)
p3 <- ggplot(seasonal_by_decade, aes(x = Month_name, y = CO2_mean,
color = Decade_label, group = Decade_label)) +
geom_line(linewidth = 1.2, alpha = 0.8) +
geom_point(size = 2.5) +
labs(
title = "Évolution du cycle saisonnier par décennie",
subtitle = "Moyenne mensuelle pour chaque décennie (1979-2025)",
x = "Mois",
y = "CO₂ (ppm)",
color = "Décennie"
) +
scale_color_viridis_d(option = "D", begin = 0.2, end = 0.9) +
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(face = "bold", hjust = 0.5, size = 15),
plot.subtitle = element_text(hjust = 0.5, color = "gray40"),
legend.position = "right",
axis.text.x = element_text(angle = 45, hjust = 1)
) +
scale_x_discrete(limits = month.abb)
print(p3)Commentaire:Ce graphique superpose les cycles saisonniers de six décennies pour montrer comment le phénomène naturel du cycle du CO₂ a évolué au fil du temps. Chaque décennie “ajoute une marche” à l’escalier du CO₂ atmosphérique, confirmant une augmentation systématique et inexorable depuis 1979.
p4 <- ggplot(co2_clean, aes(x = factor(Decade), y = CO2_ppm, fill = factor(Decade))) +
geom_boxplot(alpha = 0.7, outlier.alpha = 0.3) +
stat_summary(fun = mean, geom = "point", shape = 23, size = 3, fill = "white") +
labs(
title = "Distribution du CO₂ par décennie",
subtitle = "Boîtes: distribution interquartile | Losanges: moyenne",
x = "Décennie",
y = "CO₂ (ppm)",
fill = "Décennie"
) +
scale_fill_viridis_d(option = "C", begin = 0.2, end = 0.9) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", hjust = 0.5),
legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1)
) +
scale_y_continuous(limits = c(330, 440))
print(p4)Commentaire:Ce graphique est une transformation mathématique du précédent qui révèle une vérité importante : il montre l’anomalie du CO₂, c’est-à-dire l’écart par rapport à la normale climatologique mensuelle. En d’autres termes, il supprime les variations saisonnières pour ne montrer que le “surplus de CO₂” au-delà de ce qui est attendu pour chaque mois.
co2_monthly_avg <- co2_clean %>%
group_by(Month) %>%
summarise(monthly_avg = mean(CO2_ppm, na.rm = TRUE))
co2_anomalies <- co2_clean %>%
left_join(co2_monthly_avg, by = "Month") %>%
mutate(anomaly = CO2_ppm - monthly_avg)
p5 <- ggplot(co2_anomalies, aes(x = Date, y = anomaly)) +
geom_line(color = "#FF7F00", linewidth = 0.7, alpha = 0.7) +
geom_hline(yintercept = 0, linetype = "dashed", color = "gray50") +
geom_smooth(method = "loess", span = 0.2, color = "#A65628", se = FALSE) +
labs(
title = "Anomalies du CO₂ par rapport à la moyenne mensuelle",
subtitle = "Écart à la moyenne climatologique mensuelle",
x = "Année",
y = "Anomalie CO₂ (ppm)"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", hjust = 0.5, size = 14),
plot.subtitle = element_text(hjust = 0.5, color = "gray40")
) +
scale_x_date(date_breaks = "5 years", date_labels = "%Y")
print(p5)Commentaire:Ce graphique utilise un diagramme en boîte (boxplot) pour comparer la distribution du CO₂ à travers six décennies.Chaque génération depuis 1979 a vécu dans une atmosphère avec ~20 ppm de CO₂ supplémentaire par rapport à la génération précédente.
annual_growth <- co2_clean %>%
group_by(Year) %>%
summarise(mean_co2 = mean(CO2_ppm, na.rm = TRUE)) %>%
mutate(
growth = mean_co2 - lag(mean_co2),
growth_rate = (mean_co2 - lag(mean_co2)) / lag(mean_co2) * 100
) %>%
filter(!is.na(growth))
p6 <- ggplot(annual_growth, aes(x = Year, y = growth)) +
geom_col(fill = "#F781BF", alpha = 0.7) +
geom_hline(yintercept = mean(annual_growth$growth, na.rm = TRUE),
linetype = "dashed", color = "#E41A1C", linewidth = 1) +
geom_smooth(method = "loess", color = "#377EB8", se = FALSE, linewidth = 0.8) +
labs(
title = "Augmentation annuelle du CO₂",
subtitle = paste("Moyenne:", round(mean(annual_growth$growth, na.rm = TRUE), 2), "ppm/an"),
x = "Année",
y = "Augmentation (ppm/an)"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5, color = "gray40")
) +
scale_x_continuous(breaks = seq(1980, 2025, by = 5))
print(p6)Commentaire:Ce graphique montre l’augmentation année-par-année du CO₂. Bien que l’augmentation du CO₂ varie d’année en année, la tendance générale (ligne bleue) montre clairement une accélération du taux d’augmentation. Le problème ne s’améliore pas ; il s’aggrave. La moyenne de 1.93 ppm/an masque le fait que les années récentes augmentent plus vite (~2.7 ppm/an), ce qui indique une urgence croissante de la crise climatique.
seasonal_amplitude_by_decade <- seasonal_by_decade %>%
group_by(Decade, Decade_label) %>%
summarise(
"Amplitude (ppm)" = round(max(CO2_mean) - min(CO2_mean), 2),
"CO₂ min (ppm)" = round(min(CO2_mean), 2),
"CO₂ max (ppm)" = round(max(CO2_mean), 2),
.groups = 'drop'
) %>%
select(-Decade)
knitr::kable(seasonal_amplitude_by_decade,
caption = "Amplitude saisonnière du CO₂ par décennie") %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))| Decade_label | Amplitude (ppm) | CO₂ min (ppm) | CO₂ max (ppm) |
|---|---|---|---|
| 1970s | 3.95 | 334.37 | 338.32 |
| 1980s | 3.49 | 343.05 | 346.54 |
| 1990s | 3.77 | 357.61 | 361.38 |
| 2000s | 3.79 | 375.47 | 379.26 |
| 2010s | 4.11 | 396.54 | 400.65 |
| 2020s | 3.88 | 416.06 | 419.94 |
decade_slopes <- co2_clean %>%
group_by(Decade) %>%
do(
data.frame(
Decade = unique(.$Decade),
Pente_ppm_par_an = round(coef(lm(CO2_ppm ~ Year_fraction, data = .))[2], 4),
R_squared = round(summary(lm(CO2_ppm ~ Year_fraction, data = .))$r.squared, 4)
)
)
knitr::kable(decade_slopes,
caption = "Pente de croissance du CO₂ par décennie",
col.names = c("Décennie", "Pente (ppm/an)", "R²")) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"))| Décennie | Pente (ppm/an) | R² |
|---|---|---|
| 1970 | -1.0645 | 0.0608 |
| 1980 | 1.5231 | 0.8969 |
| 1990 | 1.4697 | 0.8652 |
| 2000 | 1.9699 | 0.9255 |
| 2010 | 2.3845 | 0.9444 |
| 2020 | 2.5396 | 0.8529 |
Tendance générale: Les concentrations de CO₂ augmentent de manière consistante et significative, avec une pente de 1.884 ppm par année.
Augmentation totale: Depuis 1979, le CO₂ a augmenté de 88.6 ppm, soit une augmentation relative de 26.3%.
Saisonnalité: L’amplitude saisonnière moyenne est de 2.84 ppm, le maximum étant atteint au Printemps et le minimum en Automne.
Accélération: L’analyse des taux de croissance annuels montre une variabilité d’une année à l’autre, avec une tendance à l’augmentation globale.
Cette analyse des données NOAA (1979-2025) démontre sans ambiguïté l’augmentation continue et significative des concentrations de CO₂ atmosphérique à l’échelle mondiale. Avec une pente linéaire de 1.88 ppm/an et un excellent ajustement statistique (R² = 0.985), les résultats confirment que cette hausse n’est pas due au hasard ou à la variabilité naturelle, mais représente une tendance structurelle majeure. L’augmentation totale de 88.6 ppm en 46 ans (26% d’accroissement relatif) constitue une signature incontestable du changement climatique anthropogénique. Bien que la saisonnalité naturelle soit préservée (cycle régulier de ~2.8 ppm), elle survient désormais sur un niveau de base en hausse permanente, escalade décennie par décennie. Ces observations confirment l’urgence d’une action globale pour réduire les émissions de CO₂, car le système climatique répond directement et proportionnellement à l’accumulation de ce gaz à effet de serre.
# Script complet de préparation des données
# 1. Packages
packages <- c("tidyverse", "lubridate", "ggplot2", "ggpubr", "zoo", "gridExtra",
"viridis", "scales", "forecast", "grid", "knitr", "kableExtra")
invisible(lapply(packages, library, character.only = TRUE))
# 2. Lecture
co2_raw <- read.table("co2_mm_gl.csv",
header = TRUE,
sep = ",",
comment.char = "#",
stringsAsFactors = FALSE,
na.strings = c("", "NA", "-99.99"))
# 3. Nettoyage
co2_clean <- co2_raw %>%
mutate(
Date = make_date(year, month, 15),
CO2_ppm = average,
CO2_trend = trend,
CO2_anomaly = average - mean(average, na.rm = TRUE),
Year = year,
Month = month,
Decade = floor(year / 10) * 10,
Year_fraction = year + (month - 0.5) / 12,
Season = case_when(
month %in% c(12, 1, 2) ~ "Hiver",
month %in% c(3, 4, 5) ~ "Printemps",
month %in% c(6, 7, 8) ~ "Été",
month %in% c(9, 10, 11) ~ "Automne"
)
) %>%
group_by(Year) %>%
mutate(
CO2_ppm = ifelse(is.na(CO2_ppm), mean(CO2_ppm, na.rm = TRUE), CO2_ppm)
) %>%
ungroup() %>%
select(Date, Year, Month, CO2_ppm, CO2_trend, CO2_anomaly, Season, Decade, Year_fraction) %>%
arrange(Date)Rapport généré le: 16 December 2025
Source des données: NOAA Global Monitoring
Laboratory
Période analysée: 1979-2025